scholarly journals Estimating Fuel Loads and Structural Characteristics of Shrub Communities by Using Terrestrial Laser Scanning

2020 ◽  
Vol 12 (22) ◽  
pp. 3704
Author(s):  
Cecilia Alonso-Rego ◽  
Stéfano Arellano-Pérez ◽  
Carlos Cabo ◽  
Celestino Ordoñez ◽  
Juan Gabriel Álvarez-González ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Goodness Of Fit ◽  
Fire Hazard ◽  
Structural Characteristics ◽  
Fire Behavior ◽  
Terrestrial Laser Scanning ◽  
Fuel Load ◽  
Fuel Loads ◽  
Litter Depth ◽  
Species Specific

Forest fuel loads and structural characteristics strongly affect fire behavior, regulating the rate of spread, fireline intensity, and flame length. Accurate fuel characterization, including disaggregation of the fuel load by size classes, is therefore essential to obtain reliable predictions from fire behavior simulators and to support decision-making in fuel management and fire hazard prediction. A total of 55 sample plots of four of the main non-tree covered shrub communities in NW Spain were non-destructively sampled to estimate litter depth and shrub cover and height for species. Fuel loads were estimated from species-specific equations. Moreover, a single terrestrial laser scanning (TLS) scan was collected in each sample plot and features related to the vertical and horizontal distribution of the cloud points were calculated. Two alternative approaches for estimating size-disaggregated fuel loads and live/dead fractions from TLS data were compared: (i) a two-steps indirect estimation approach (IE) based on fitting three equations to estimate shrub height and cover and litter depth from TLS data and then use those estimates as inputs of the existing species-specific fuel load equations by size fractions based on these three variables; and (ii) a direct estimation approach (DE), consisting of fitting seven equations, one for each fuel fraction, to relate the fuel load estimates to TLS data. Overall, the direct approach produced more balanced goodness-of-fit statistics for the seven fractions considered jointly, suggesting that it performed better than the indirect approach, with equations explaining more than 80% of the observed variability for all species and fractions, except the litter loads.

scholarly journals No evidence of increased fire risk due to agricultural land abandonment in Sardinia (Italy)

2012 ◽  
Vol 12 (5) ◽  
pp. 1333-1336 ◽  
Author(s):  
C. Ricotta ◽  
D. Guglietta ◽  
A. Migliozzi
Keyword(s):  
Rural Areas ◽  
Agricultural Land ◽  
Plant Biomass ◽  
Fire Hazard ◽  
Fire Behavior ◽  
Land Abandonment ◽  
Fuel Load ◽  
Short Paper ◽  
Ignition Probability ◽  
Fire Ignition

Abstract. Different land cover types are related to different levels of fire hazard through their vegetation structure and fuel load composition. Therefore, understanding the relationships between landscape changes and fire behavior is of crucial importance for developing adequate fire fighting and fire prevention strategies for a changing world. In the last decades the abandonment of agricultural lands and pastoral activities has been the major driver of landscape transformations in Mediterranean Europe. As agricultural land abandonment typically promotes an increase in plant biomass (fuel load), a number of authors argue that vegetation succession in abandoned fields and pastures is expected to increase fire hazard. In this short paper, based on 28 493 fires in Sardinia (Italy) in the period 2001–2010, we show that there is no evidence of increased probability of fire ignition in abandoned rural areas. To the contrary, in Sardinia the decreased human impact associated with agricultural land abandonment leads to a statistically significant decrease of fire ignition probability.

Research on the Point Cloud Data Processing Method of 3D Terrestrial Laser Scanning in Existing Railway Track

2015 ◽  
Vol 744-746 ◽  
pp. 1298-1302 ◽  
Author(s):  
Feng Han ◽  
Xiao Feng Duan
Keyword(s):  
Laser Scanning ◽  
Structural Characteristics ◽  
Data Extraction ◽  
Three Dimensional ◽  
Terrestrial Laser Scanning ◽  
Railway Track ◽  
Cloud Data ◽  
Railway Line ◽  
Data Processing Method ◽  
Static Detection

Characterized with efficient, accurate and non-contact measurement, and the fast and three-dimensional visualization features, using 3D terrestrial laser scanning technology in track static detection has attracted widespread attention. Based on the structural characteristics of the railway line, use Geomagic software and Cyclone software in the pre-processing stage, remove the noise and redundancy, package the data after registration, get the initial line model finally. In the data extraction stage, combined with professional needs, respectively research the data extraction of track pitch and direction, and the bed section, from line, plane, and body. Which have provided a good research idea for using 3D terrestrial laser scanning technology in track static detection, acceptance, and some other aspects.

scholarly journals Assessment of Fire Fuel Load Dynamics in Shrubland Ecosystems in the Western United States Using MODIS Products

2020 ◽  
Vol 12 (12) ◽  
pp. 1911
Author(s):  
Zhengpeng Li ◽  
Hua Shi ◽  
James E. Vogelmann ◽  
Todd J. Hawbaker ◽  
Birgit Peterson
Keyword(s):  
United States ◽  
Net Primary Production ◽  
Fire Behavior ◽  
Western United States ◽  
Fuel Load ◽  
High Temporal Resolution ◽  
Spectral Variability ◽  
Fuel Loads ◽  
Grassland Ecosystems ◽  
Fuel Models

Assessing fire behavior in shrubland/grassland ecosystems of the western United States has proven especially problematic, in part due to the complex nature of the vegetation and its relationships with prior fire history events. Our goals in this study were (1) to determine if we can effectively leverage the high temporal resolution capabilities of current remote sensing systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) to improve upon shrub and grassland mapping and (2) to determine if these improvements alter and improve fire behavior model results in these grass- and shrub-dominated systems. The study focused on the shrublands and grasslands of the Owyhee Basin, which is located primarily in southern Idaho. Shrubland and grassland fuel load dynamics were characterized using Normalized Difference Vegetation Index (NDVI) and Net Primary Production (NPP) datasets (both derived from MODIS). NDVI shrub and grassland values were converted to biomass, and custom fire behavior fuel models were then developed to evaluate the impacts of surface fuel changes on fire behaviors. Results from the study include the following: (1) high intra- and interannual spectral variability characterized these shrubland/grassland ecosystems, and this spectral variability was highly correlated with climate variables, most notably precipitation; (2) fire activity had a higher likelihood of occurring in areas where the NDVI (and biomass) differential between spring and summer values was especially high; (3) the annual fuel loads estimated from MODIS NPP showed that live herbaceous fuel loads were closely correlated with annual precipitation; (4) estimated fuel load accumulation was higher on shrublands than grasslands with the same vegetation productivity; (5) the total fuel load on shrublands was impacted by shrubland age, and live woody fuel load was over 66% of the total fuel load; and (6) comparisons of simulated fire behavior and spread between dynamic and static fuel loads, the latter estimates being obtained from the operational and nationwide LANDFIRE program, showed clear differences in fire indices and fire burn areas between the dynamic fuel loads and the static fuel loads. Current standard fuel models appear to have bias in underestimating the fire spread and total burnable area.

scholarly journals Coupling Terrestrial Laser Scanning with 3D Fuel Biomass Sampling for Advancing Wildland Fuels Characterization

2019 ◽  
Author(s):  
Eric Rowell ◽  
E. Louise Loudermilk ◽  
Christie Hawley ◽  
Scott Pokswinski ◽  
Carl Seielstad ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Wildland Fire ◽  
Fire Behavior ◽  
Terrestrial Laser Scanning ◽  
Sampling Technique ◽  
Fire Effects ◽  
Three Dimensions ◽  
Fine Scale ◽  
Field Sampling ◽  
Volume Porosity

AbstractThe spatial pattern of surface fuelbeds in fire-dependent ecosystems are rarely captured using long-standing fuel sampling methods. New techniques, both field sampling and remote sensing, that capture vegetation fuel type, biomass, and volume at super fine-scales (cm to dm) in three-dimensions (3D) are critical to advancing forest fuel and wildland fire science. This is particularly true for computational fluid dynamics fire behavior models that operate in 3D and have implications for wildland fire operations and fire effects research. This study describes the coupling of new 3D field sampling data with terrestrial laser scanning (TLS) data to infer fine-scale fuel mass in 3D. We found that there are strong relationships between fine-scale mass and TLS occupied volume, porosity, and surface area, which were used to develop fine-scale prediction equations using TLS across vegetative fuel types, namely grasses and shrubs. The application of this novel 3D sampling technique to high resolution TLS data in this study represents a major advancement in understanding fire-vegetation feedbacks in highly managed fire-dependent ecosystems.

scholarly journals Potential of Sentinel-2A Data to Model Surface and Canopy Fuel Characteristics in Relation to Crown Fire Hazard

2018 ◽  
Vol 10 (10) ◽  
pp. 1645 ◽  
Author(s):  
Stéfano Arellano-Pérez ◽  
Fernando Castedo-Dorado ◽  
Carlos López-Sánchez ◽  
Eduardo González-Ferreiro ◽  
Zhiqiang Yang ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Fire Hazard ◽  
Confusion Matrix ◽  
Multivariate Adaptive Regression Splines ◽  
Machine Learning Techniques ◽  
Fuel Load ◽  
Crown Fire ◽  
Potential Type ◽  
Crown Fires ◽  
Sentinel 2A

Background: Crown fires are often intense and fast spreading and hence can have serious impacts on soil, vegetation, and wildlife habitats. Fire managers try to prevent the initiation and spread of crown fires in forested landscapes through fuel management. The minimum fuel conditions necessary to initiate and propagate crown fires are known to be strongly influenced by four stand structural variables: surface fuel load (SFL), fuel strata gap (FSG), canopy base height (CBH), and canopy bulk density (CBD). However, there is often a lack of quantitative data about these variables, especially at the landscape scale. Methods: In this study, data from 123 sample plots established in pure, even-aged, Pinus radiata and Pinus pinaster stands in northwest Spain were analyzed. In each plot, an intensive field inventory was used to characterize surface and canopy fuels load and structure, and to estimate SFL, FSG, CBH, and CBD. Equations relating these variables to Sentinel-2A (S-2A) bands and vegetation indices were obtained using two non-parametric techniques: Random Forest (RF) and Multivariate Adaptive Regression Splines (MARS). Results: According to the goodness-of-fit statistics, RF models provided the most accurate estimates, explaining more than 12%, 37%, 47%, and 31% of the observed variability in SFL, FSG, CBH, and CBD, respectively. To evaluate the performance of the four equations considered, the observed and estimated values of the four fuel variables were used separately to predict the potential type of wildfire (surface fire, passive crown fire, or active crown fire) for each plot, considering three different burning conditions (low, moderate, and extreme). The results of the confusion matrix indicated that 79.8% of the surface fires and 93.1% of the active crown fires were correctly classified; meanwhile, the highest rate of misclassification was observed for passive crown fire, with 75.6% of the samples correctly classified. Conclusions: The results highlight that the combination of medium resolution imagery and machine learning techniques may add valuable information about surface and canopy fuel variables at large scales, whereby crown fire potential and the potential type of wildfire can be classified.

scholarly journals New 3D measurements of large redwood trees for biomass and structure

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mathias Disney ◽  
Andrew Burt ◽  
Phil Wilkes ◽  
John Armston ◽  
Laura Duncanson
Keyword(s):  
Laser Scanning ◽  
3D Structure ◽  
Terrestrial Laser Scanning ◽  
Specific Model ◽  
Observation Data ◽  
Ecosystem Structure ◽  
Above Ground Biomass ◽  
Large Trees ◽  
Earth Observation Data ◽  
Species Specific

Abstract Large trees are disproportionately important in terms of their above ground biomass (AGB) and carbon storage, as well as their wider impact on ecosystem structure. They are also very hard to measure and so tend to be underrepresented in measurements and models of AGB. We show the first detailed 3D terrestrial laser scanning (TLS) estimates of the volume and AGB of large coastal redwood Sequoia sempervirens trees from three sites in Northern California, representing some of the highest biomass ecosystems on Earth. Our TLS estimates agree to within 2% AGB with a species-specific model based on detailed manual crown mapping of 3D tree structure. However TLS-derived AGB was more than 30% higher compared to widely-used general (non species-specific) allometries. We derive an allometry from TLS that spans a much greater range of tree size than previous models and so is potentially better-suited for use with new Earth Observation data for these exceptionally high biomass areas. We suggest that where possible, TLS and crown mapping should be used to provide complementary, independent 3D structure measurements of these very large trees.

scholarly journals AEGIS: a wildfire prevention and management information system

2016 ◽  
Vol 16 (3) ◽  
pp. 643-661 ◽  
Author(s):  
Kostas Kalabokidis ◽  
Alan Ager ◽  
Mark Finney ◽  
Nikos Athanasis ◽  
Palaiologos Palaiologou ◽  
...  
Keyword(s):  
Fire Hazard ◽  
Fire Behavior ◽  
End Users ◽  
Burned Area ◽  
Fire Control ◽  
Web Based ◽  
Ignition Point ◽  
Weather Information ◽  
Management Platform ◽  
Burn Probability

Abstract. We describe a Web-GIS wildfire prevention and management platform (AEGIS) developed as an integrated and easy-to-use decision support tool to manage wildland fire hazards in Greece (http://aegis.aegean.gr). The AEGIS platform assists with early fire warning, fire planning, fire control and coordination of firefighting forces by providing online access to information that is essential for wildfire management. The system uses a number of spatial and non-spatial data sources to support key system functionalities. Land use/land cover maps were produced by combining field inventory data with high-resolution multispectral satellite images (RapidEye). These data support wildfire simulation tools that allow the users to examine potential fire behavior and hazard with the Minimum Travel Time fire spread algorithm. End-users provide a minimum number of inputs such as fire duration, ignition point and weather information to conduct a fire simulation. AEGIS offers three types of simulations, i.e., single-fire propagation, point-scale calculation of potential fire behavior, and burn probability analysis, similar to the FlamMap fire behavior modeling software. Artificial neural networks (ANNs) were utilized for wildfire ignition risk assessment based on various parameters, training methods, activation functions, pre-processing methods and network structures. The combination of ANNs and expected burned area maps are used to generate integrated output map of fire hazard prediction. The system also incorporates weather information obtained from remote automatic weather stations and weather forecast maps. The system and associated computation algorithms leverage parallel processing techniques (i.e., High Performance Computing and Cloud Computing) that ensure computational power required for real-time application. All AEGIS functionalities are accessible to authorized end-users through a web-based graphical user interface. An innovative smartphone application, AEGIS App, also provides mobile access to the web-based version of the system.

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